Watanabe Keita, Haga Tatsuya, Tatsuno Masami, Euston David R, Fukai Tomoki
Department of Complexity Science and Engineering, University of Tokyo, Kashiwa, Japan.
RIKEN Center for Brain Science, Wako, Japan.
Front Neuroinform. 2019 May 31;13:39. doi: 10.3389/fninf.2019.00039. eCollection 2019.
Neurons which fire in a fixed temporal pattern (i.e., "cell assemblies") are hypothesized to be a fundamental unit of neural information processing. Several methods are available for the detection of cell assemblies without a time structure. However, the systematic detection of cell assemblies with time structure has been challenging, especially in large datasets, due to the lack of efficient methods for handling the time structure. Here, we show a method to detect a variety of cell-assembly activity patterns, recurring in noisy neural population activities at multiple timescales. The key innovation is the use of a computer science method to comparing strings ("edit similarity"), to group spikes into assemblies. We validated the method using artificial data and experimental data, which were previously recorded from the hippocampus of male Long-Evans rats and the prefrontal cortex of male Brown Norway/Fisher hybrid rats. From the hippocampus, we could simultaneously extract place-cell sequences occurring on different timescales during navigation and awake replay. From the prefrontal cortex, we could discover multiple spike sequences of neurons encoding different segments of a goal-directed task. Unlike conventional event-driven statistical approaches, our method detects cell assemblies without creating event-locked averages. Thus, the method offers a novel analytical tool for deciphering the neural code during arbitrary behavioral and mental processes.
以固定时间模式放电的神经元（即“细胞集合”）被假定为神经信息处理的基本单元。有几种方法可用于检测没有时间结构的细胞集合。然而，由于缺乏处理时间结构的有效方法，对具有时间结构的细胞集合进行系统检测一直具有挑战性，尤其是在大型数据集中。在这里，我们展示了一种检测多种细胞集合活动模式的方法，这些模式在多个时间尺度上的嘈杂神经群体活动中反复出现。关键创新在于使用一种计算机科学方法来比较字符串（“编辑相似度”），以将尖峰分组为集合。我们使用人工数据和实验数据验证了该方法，这些数据先前记录于雄性长 Evans 大鼠的海马体以及雄性布朗挪威/费希尔杂交大鼠的前额叶皮层。从海马体中，我们可以在导航和清醒回放期间同时提取不同时间尺度上出现的位置细胞序列。从前额叶皮层中，我们可以发现编码目标导向任务不同片段的神经元的多个尖峰序列。与传统的事件驱动统计方法不同，我们的方法在不创建事件锁定平均值的情况下检测细胞集合。因此，该方法为在任意行为和心理过程中解读神经编码提供了一种新颖的分析工具。
